RESUMO
This paper presents an innovative fusion model called "CALSE-LSTM," which integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), self-attention mechanisms, and squeeze-and-excitation attention mechanisms to optimize the estimation accuracy of the State of Charge (SoC). The model incorporates battery historical data as input and employs a dual-attention mechanism based on CNN-LSTM to extract diverse features from the input data, thereby enhancing the model's ability to learn hidden information. To further improve model performance, we fine-tune the model parameters using the Pelican algorithm. Experiments conducted under Urban Dynamometer Driving Schedule (UDDS) conditions show that the CALSE-LSTM model achieves a Root Mean Squared Error (RMSE) of only 1.73 % in lithium battery SoC estimation, significantly better than GRU, LSTM, and CNN-LSTM models, reducing errors by 31.9 %, 31.3 %, and 15 %, respectively. Ablation experiments further confirm the effectiveness of the dual-attention mechanism and its potential to improve SoC estimation performance. Additionally, we validate the learning efficiency of CALSE-LSTM by comparing model training time with the number of iterations. Finally, in the comparative experiment with the Kalman filtering method, the model in this paper significantly improved its performance by incorporating power consumption as an additional feature input. This further verifies the accuracy of CALSE-LSTM in estimating the State of Charge (SoC) of lithium batteries.